1.2k citations · 1.6k across the 23 of their papers we have counts for
19 papers · 1 filter
LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation
Jiawei Xu, Zongqing Lu, Qingmin Liao
Lack of texture often causes ambiguity in matching, and handling this issue is an important challenge in optical flow estimation. Some methods insert stacked transformer modules th…
IRGen: Generative Modeling for Image Retrieval
Yidan Zhang, Ting Zhang, Dong Chen +11
While generative modeling has become prevalent across numerous research fields, its integration into the realm of image retrieval remains largely unexplored and underjustified. In…
Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection
Yanjun Liu, Wenming Yang
Indoor 3D object detection is an essential task in single image scene understanding, impacting spatial cognition fundamentally in visual reasoning. Existing works on 3D object dete…
Meta-Learning based Degradation Representation for Blind Super-Resolution
Bin Xia, Yapeng Tian, Yulun Zhang +3
The most of CNN based super-resolution (SR) methods assume that the degradation is known (\eg, bicubic). These methods will suffer a severe performance drop when the degradation is…
SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization
Yucheng Hang, Bin Xia, Wenming Yang +1
Image harmonization aims to achieve visual consistency in composite images by adapting a foreground to make it compatible with a background. However, existing methods always only u…
STDAN: Deformable Attention Network for Space-Time Video Super-Resolution
Hai Wang, Xiaoyu Xiang, Yapeng Tian +2
The target of space-time video super-resolution (STVSR) is to increase the spatial-temporal resolution of low-resolution (LR) and low frame rate (LFR) videos. Recent approaches bas…
Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution
Bin Xia, Yapeng Tian, Yucheng Hang +3
Reference-based super-resolution (RefSR) has made significant progress in producing realistic textures using an external reference (Ref) image. However, existing RefSR methods obta…
Efficient Non-Local Contrastive Attention for Image Super-Resolution
Bin Xia, Yucheng Hang, Yapeng Tian +3
Non-Local Attention (NLA) brings significant improvement for Single Image Super-Resolution (SISR) by leveraging intrinsic feature correlation in natural images. However, NLA gives…
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation
Jiacheng Chen, Bin-Bin Gao, Zongqing Lu +3
Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning…
Group Fisher Pruning for Practical Network Compression
Liyang Liu, Shilong Zhang, Zhanghui Kuang +7
Network compression has been widely studied since it is able to reduce the memory and computation cost during inference. However, previous methods seldom deal with complicated stru…
SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes
Jiacheng Chen, Bin-Bin Gao, Zongqing Lu +3
Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples in support images. Most of advanced solutions exploit a metri…
A region-based descriptor network for uniformly sampled keypoints
Kai Lv, Zongqing Lu, Qingmin Liao
Matching keypoint pairs of different images is a basic task of computer vision. Most methods require customized extremum point schemes to obtain the coordinates of feature points w…
Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth
Juncheng Zhang, Qingmin Liao, Shaojun Liu +3
Multi-focus image fusion, a technique to generate an all-in-focus image from two or more partially-focused source images, can benefit many computer vision tasks. However, currently…
XSepConv: Extremely Separated Convolution
Jiarong Chen, Zongqing Lu, Jing-Hao Xue +1
Depthwise convolution has gradually become an indispensable operation for modern efficient neural networks and larger kernel sizes () have been applied to it recently. In thi…
An α-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion
Haoyu Ma, Qingmin Liao, Juncheng Zhang +2
Capturing an all-in-focus image with a single camera is difficult since the depth of field of the camera is usually limited. An alternative method to obtain the all-in-focus image…
Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network
Haoyu Ma, Juncheng Zhang, Shaojun Liu +1
Since it is usually difficult to capture an all-in-focus image of a 3D scene directly, various multi-focus image fusion methods are employed to generate it from several images focu…
Lightweight Feature Fusion Network for Single Image Super-Resolution
Wenming Yang, Wei Wang, Xuechen Zhang +2
Single image super-resolution(SISR) has witnessed great progress as convolutional neural network(CNN) gets deeper and wider. However, enormous parameters hinder its application to…
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network
Liping Zhang, Zongqing Lu, Qingmin Liao
The convolutional neural network model for optical flow estimation usually outputs a low-resolution(LR) optical flow field. To obtain the corresponding full image resolution,interp…
Deep Learning for Single Image Super-Resolution: A Brief Review
Wenming Yang, Xuechen Zhang, Yapeng Tian +2
Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions…